Overview

Dataset statistics

Number of variables23
Number of observations23699
Missing cells101441
Missing cells (%)18.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.2 MiB
Average record size in memory364.3 B

Variable types

Numeric16
Categorical4
Boolean3

Alerts

first_day_exposition has a high cardinality: 1491 distinct values High cardinality
locality_name has a high cardinality: 364 distinct values High cardinality
last_price is highly correlated with total_area and 4 other fieldsHigh correlation
total_area is highly correlated with last_price and 3 other fieldsHigh correlation
rooms is highly correlated with last_price and 2 other fieldsHigh correlation
ceiling_height is highly correlated with last_price and 1 other fieldsHigh correlation
floors_total is highly correlated with floorHigh correlation
living_area is highly correlated with last_price and 2 other fieldsHigh correlation
floor is highly correlated with floors_totalHigh correlation
kitchen_area is highly correlated with last_price and 2 other fieldsHigh correlation
last_price is highly correlated with total_area and 2 other fieldsHigh correlation
total_area is highly correlated with last_price and 3 other fieldsHigh correlation
rooms is highly correlated with total_area and 1 other fieldsHigh correlation
floors_total is highly correlated with floorHigh correlation
living_area is highly correlated with last_price and 2 other fieldsHigh correlation
floor is highly correlated with floors_totalHigh correlation
kitchen_area is highly correlated with last_price and 1 other fieldsHigh correlation
last_price is highly correlated with total_areaHigh correlation
total_area is highly correlated with last_price and 2 other fieldsHigh correlation
rooms is highly correlated with total_area and 1 other fieldsHigh correlation
living_area is highly correlated with total_area and 1 other fieldsHigh correlation
last_price is highly correlated with total_area and 2 other fieldsHigh correlation
total_area is highly correlated with last_price and 3 other fieldsHigh correlation
rooms is highly correlated with last_price and 3 other fieldsHigh correlation
floors_total is highly correlated with floorHigh correlation
living_area is highly correlated with last_price and 3 other fieldsHigh correlation
floor is highly correlated with floors_totalHigh correlation
kitchen_area is highly correlated with total_area and 2 other fieldsHigh correlation
airports_nearest is highly correlated with cityCenters_nearestHigh correlation
cityCenters_nearest is highly correlated with airports_nearestHigh correlation
parks_around3000 is highly correlated with parks_nearestHigh correlation
parks_nearest is highly correlated with parks_around3000High correlation
ceiling_height has 9195 (38.8%) missing values Missing
living_area has 1903 (8.0%) missing values Missing
is_apartment has 20924 (88.3%) missing values Missing
kitchen_area has 2278 (9.6%) missing values Missing
balcony has 11519 (48.6%) missing values Missing
airports_nearest has 5542 (23.4%) missing values Missing
cityCenters_nearest has 5519 (23.3%) missing values Missing
parks_around3000 has 5518 (23.3%) missing values Missing
parks_nearest has 15620 (65.9%) missing values Missing
ponds_around3000 has 5518 (23.3%) missing values Missing
ponds_nearest has 14589 (61.6%) missing values Missing
days_exposition has 3181 (13.4%) missing values Missing
last_price is highly skewed (γ1 = 25.80427519) Skewed
ceiling_height is highly skewed (γ1 = 41.70907732) Skewed
Unnamed: 0 is uniformly distributed Uniform
Unnamed: 0 has unique values Unique
total_images has 1059 (4.5%) zeros Zeros
balcony has 3758 (15.9%) zeros Zeros

Reproduction

Analysis started2022-04-16 19:42:09.130055
Analysis finished2022-04-16 19:43:02.950568
Duration53.82 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ≥0)

UNIFORM
UNIQUE

Distinct23699
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11849
Minimum0
Maximum23698
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size185.3 KiB
2022-04-16T21:43:03.148867image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1184.9
Q15924.5
median11849
Q317773.5
95-th percentile22513.1
Maximum23698
Range23698
Interquartile range (IQR)11849

Descriptive statistics

Standard deviation6841.456351
Coefficient of variation (CV)0.5773868133
Kurtosis-1.2
Mean11849
Median Absolute Deviation (MAD)5925
Skewness0
Sum280809451
Variance46805525
MonotonicityStrictly increasing
2022-04-16T21:43:03.395241image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20471
 
< 0.1%
6611
 
< 0.1%
190841
 
< 0.1%
170371
 
< 0.1%
231821
 
< 0.1%
211351
 
< 0.1%
108961
 
< 0.1%
88491
 
< 0.1%
149941
 
< 0.1%
129471
 
< 0.1%
Other values (23689)23689
> 99.9%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
236981
< 0.1%
236971
< 0.1%
236961
< 0.1%
236951
< 0.1%
236941
< 0.1%
236931
< 0.1%
236921
< 0.1%
236911
< 0.1%
236901
< 0.1%
236891
< 0.1%

total_images
Real number (ℝ≥0)

ZEROS

Distinct38
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.858475041
Minimum0
Maximum50
Zeros1059
Zeros (%)4.5%
Negative0
Negative (%)0.0%
Memory size185.3 KiB
2022-04-16T21:43:03.619132image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median9
Q314
95-th percentile20
Maximum50
Range50
Interquartile range (IQR)8

Descriptive statistics

Standard deviation5.682528956
Coefficient of variation (CV)0.5764105435
Kurtosis-0.3359698028
Mean9.858475041
Median Absolute Deviation (MAD)4
Skewness0.2585928567
Sum233636
Variance32.29113534
MonotonicityNot monotonic
2022-04-16T21:43:03.832779image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
101798
 
7.6%
91725
 
7.3%
201694
 
7.1%
81585
 
6.7%
71521
 
6.4%
61482
 
6.3%
111362
 
5.7%
51301
 
5.5%
121225
 
5.2%
01059
 
4.5%
Other values (28)8947
37.8%
ValueCountFrequency (%)
01059
4.5%
1872
3.7%
2640
 
2.7%
3769
3.2%
4986
4.2%
51301
5.5%
61482
6.3%
71521
6.4%
81585
6.7%
91725
7.3%
ValueCountFrequency (%)
503
< 0.1%
421
 
< 0.1%
391
 
< 0.1%
371
 
< 0.1%
352
< 0.1%
324
< 0.1%
312
< 0.1%
302
< 0.1%
293
< 0.1%
284
< 0.1%

last_price
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct2978
Distinct (%)12.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6541548.772
Minimum12190
Maximum763000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size185.3 KiB
2022-04-16T21:43:04.090909image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum12190
5-th percentile1870000
Q13400000
median4650000
Q36800000
95-th percentile15300000
Maximum763000000
Range762987810
Interquartile range (IQR)3400000

Descriptive statistics

Standard deviation10887013.27
Coefficient of variation (CV)1.664286799
Kurtosis1277.682584
Mean6541548.772
Median Absolute Deviation (MAD)1500000
Skewness25.80427519
Sum1.550281643 × 1011
Variance1.185270579 × 1014
MonotonicityNot monotonic
2022-04-16T21:43:04.329042image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4500000342
 
1.4%
3500000291
 
1.2%
4000000260
 
1.1%
4300000260
 
1.1%
4200000259
 
1.1%
3600000257
 
1.1%
3300000244
 
1.0%
3800000240
 
1.0%
3200000238
 
1.0%
3700000234
 
1.0%
Other values (2968)21074
88.9%
ValueCountFrequency (%)
121901
 
< 0.1%
4300002
< 0.1%
4400001
 
< 0.1%
4500004
< 0.1%
4700003
< 0.1%
4800001
 
< 0.1%
4900002
< 0.1%
5000004
< 0.1%
5200001
 
< 0.1%
5300001
 
< 0.1%
ValueCountFrequency (%)
7630000001
< 0.1%
4200000001
< 0.1%
4013000001
< 0.1%
3300000001
< 0.1%
3000000001
< 0.1%
2892384001
< 0.1%
2450000001
< 0.1%
2400000001
< 0.1%
2300000001
< 0.1%
1908700001
< 0.1%

total_area
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2182
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60.348651
Minimum12
Maximum900
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size185.3 KiB
2022-04-16T21:43:04.578983image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile31
Q140
median52
Q369.9
95-th percentile116
Maximum900
Range888
Interquartile range (IQR)29.9

Descriptive statistics

Standard deviation35.6540829
Coefficient of variation (CV)0.5908016553
Kurtosis47.52149263
Mean60.348651
Median Absolute Deviation (MAD)13.9
Skewness4.768597224
Sum1430202.68
Variance1271.213628
MonotonicityNot monotonic
2022-04-16T21:43:04.797682image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45419
 
1.8%
42383
 
1.6%
60347
 
1.5%
31346
 
1.5%
44345
 
1.5%
40315
 
1.3%
43301
 
1.3%
32289
 
1.2%
46282
 
1.2%
36280
 
1.2%
Other values (2172)20392
86.0%
ValueCountFrequency (%)
121
 
< 0.1%
133
< 0.1%
13.21
 
< 0.1%
141
 
< 0.1%
152
< 0.1%
15.51
 
< 0.1%
161
 
< 0.1%
172
< 0.1%
17.21
 
< 0.1%
17.61
 
< 0.1%
ValueCountFrequency (%)
9001
< 0.1%
631.21
< 0.1%
6311
< 0.1%
6181
< 0.1%
5901
< 0.1%
5171
< 0.1%
5071
< 0.1%
5002
< 0.1%
4951
< 0.1%
494.11
< 0.1%

first_day_exposition
Categorical

HIGH CARDINALITY

Distinct1491
Distinct (%)6.3%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
2018-02-01T00:00:00
 
368
2017-11-10T00:00:00
 
240
2017-10-13T00:00:00
 
124
2017-09-27T00:00:00
 
111
2018-03-26T00:00:00
 
97
Other values (1486)
22759 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique118 ?
Unique (%)0.5%

Sample

1st row2019-03-07T00:00:00
2nd row2018-12-04T00:00:00
3rd row2015-08-20T00:00:00
4th row2015-07-24T00:00:00
5th row2018-06-19T00:00:00

Common Values

ValueCountFrequency (%)
2018-02-01T00:00:00368
 
1.6%
2017-11-10T00:00:00240
 
1.0%
2017-10-13T00:00:00124
 
0.5%
2017-09-27T00:00:00111
 
0.5%
2018-03-26T00:00:0097
 
0.4%
2018-07-10T00:00:0093
 
0.4%
2017-09-28T00:00:0074
 
0.3%
2018-03-06T00:00:0072
 
0.3%
2018-02-08T00:00:0071
 
0.3%
2018-02-20T00:00:0070
 
0.3%
Other values (1481)22379
94.4%

Length

2022-04-16T21:43:05.000760image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2018-02-01t00:00:00368
 
1.6%
2017-11-10t00:00:00240
 
1.0%
2017-10-13t00:00:00124
 
0.5%
2017-09-27t00:00:00111
 
0.5%
2018-03-26t00:00:0097
 
0.4%
2018-07-10t00:00:0093
 
0.4%
2017-09-28t00:00:0074
 
0.3%
2018-03-06t00:00:0072
 
0.3%
2018-02-08t00:00:0071
 
0.3%
2018-02-20t00:00:0070
 
0.3%
Other values (1481)22379
94.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

rooms
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct17
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.070635892
Minimum0
Maximum19
Zeros197
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size185.3 KiB
2022-04-16T21:43:05.191760image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q33
95-th percentile4
Maximum19
Range19
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.078404851
Coefficient of variation (CV)0.5208085377
Kurtosis8.689136218
Mean2.070635892
Median Absolute Deviation (MAD)1
Skewness1.524982284
Sum49072
Variance1.162957022
MonotonicityNot monotonic
2022-04-16T21:43:05.319422image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
18047
34.0%
27940
33.5%
35814
24.5%
41180
 
5.0%
5326
 
1.4%
0197
 
0.8%
6105
 
0.4%
759
 
0.2%
812
 
0.1%
98
 
< 0.1%
Other values (7)11
 
< 0.1%
ValueCountFrequency (%)
0197
 
0.8%
18047
34.0%
27940
33.5%
35814
24.5%
41180
 
5.0%
5326
 
1.4%
6105
 
0.4%
759
 
0.2%
812
 
0.1%
98
 
< 0.1%
ValueCountFrequency (%)
191
 
< 0.1%
161
 
< 0.1%
151
 
< 0.1%
142
 
< 0.1%
121
 
< 0.1%
112
 
< 0.1%
103
 
< 0.1%
98
 
< 0.1%
812
 
0.1%
759
0.2%

ceiling_height
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
SKEWED

Distinct183
Distinct (%)1.3%
Missing9195
Missing (%)38.8%
Infinite0
Infinite (%)0.0%
Mean2.771498897
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size185.3 KiB
2022-04-16T21:43:05.462717image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.5
Q12.52
median2.65
Q32.8
95-th percentile3.3
Maximum100
Range99
Interquartile range (IQR)0.28

Descriptive statistics

Standard deviation1.261055831
Coefficient of variation (CV)0.4550085993
Kurtosis2627.139521
Mean2.771498897
Median Absolute Deviation (MAD)0.15
Skewness41.70907732
Sum40197.82
Variance1.590261809
MonotonicityNot monotonic
2022-04-16T21:43:05.749784image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.53515
 
14.8%
2.61646
 
6.9%
2.71574
 
6.6%
31112
 
4.7%
2.8993
 
4.2%
2.55980
 
4.1%
2.75910
 
3.8%
2.65676
 
2.9%
3.2277
 
1.2%
3.1203
 
0.9%
Other values (173)2618
 
11.0%
(Missing)9195
38.8%
ValueCountFrequency (%)
11
 
< 0.1%
1.21
 
< 0.1%
1.751
 
< 0.1%
211
< 0.1%
2.21
 
< 0.1%
2.251
 
< 0.1%
2.34
 
< 0.1%
2.341
 
< 0.1%
2.423
0.1%
2.4515
0.1%
ValueCountFrequency (%)
1001
 
< 0.1%
322
 
< 0.1%
27.51
 
< 0.1%
278
< 0.1%
261
 
< 0.1%
257
< 0.1%
241
 
< 0.1%
22.61
 
< 0.1%
201
 
< 0.1%
141
 
< 0.1%

floors_total
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct36
Distinct (%)0.2%
Missing86
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean10.67382374
Minimum1
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size185.3 KiB
2022-04-16T21:43:05.884369image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q15
median9
Q316
95-th percentile25
Maximum60
Range59
Interquartile range (IQR)11

Descriptive statistics

Standard deviation6.597172989
Coefficient of variation (CV)0.6180702576
Kurtosis0.04464170398
Mean10.67382374
Median Absolute Deviation (MAD)4
Skewness0.9402749458
Sum252041
Variance43.52269145
MonotonicityNot monotonic
2022-04-16T21:43:06.014761image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
55788
24.4%
93761
15.9%
161376
 
5.8%
121362
 
5.7%
41200
 
5.1%
101174
 
5.0%
251075
 
4.5%
6914
 
3.9%
17833
 
3.5%
3668
 
2.8%
Other values (26)5462
23.0%
ValueCountFrequency (%)
125
 
0.1%
2383
 
1.6%
3668
 
2.8%
41200
 
5.1%
55788
24.4%
6914
 
3.9%
7592
 
2.5%
8390
 
1.6%
93761
15.9%
101174
 
5.0%
ValueCountFrequency (%)
601
 
< 0.1%
521
 
< 0.1%
371
 
< 0.1%
363
 
< 0.1%
3524
 
0.1%
341
 
< 0.1%
331
 
< 0.1%
291
 
< 0.1%
2821
 
0.1%
27164
0.7%

living_area
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct1782
Distinct (%)8.2%
Missing1903
Missing (%)8.0%
Infinite0
Infinite (%)0.0%
Mean34.45785243
Minimum2
Maximum409.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size185.3 KiB
2022-04-16T21:43:06.165719image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile15.2
Q118.6
median30
Q342.3
95-th percentile69
Maximum409.7
Range407.7
Interquartile range (IQR)23.7

Descriptive statistics

Standard deviation22.03044522
Coefficient of variation (CV)0.6393446969
Kurtosis31.36088975
Mean34.45785243
Median Absolute Deviation (MAD)11.8
Skewness3.909429763
Sum751043.3515
Variance485.3405164
MonotonicityNot monotonic
2022-04-16T21:43:06.316502image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18882
 
3.7%
17675
 
2.8%
30598
 
2.5%
16486
 
2.1%
20481
 
2.0%
28423
 
1.8%
31381
 
1.6%
19329
 
1.4%
32320
 
1.4%
29319
 
1.3%
Other values (1772)16902
71.3%
(Missing)1903
 
8.0%
ValueCountFrequency (%)
22
< 0.1%
32
< 0.1%
51
< 0.1%
5.41
< 0.1%
61
< 0.1%
6.51
< 0.1%
82
< 0.1%
8.31
< 0.1%
8.41
< 0.1%
8.51
< 0.1%
ValueCountFrequency (%)
409.71
< 0.1%
4091
< 0.1%
347.51
< 0.1%
3321
< 0.1%
322.31
< 0.1%
312.51
< 0.1%
301.51
< 0.1%
3001
< 0.1%
279.61
< 0.1%
2741
< 0.1%

floor
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct33
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.892358327
Minimum1
Maximum33
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size185.3 KiB
2022-04-16T21:43:06.472708image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q38
95-th percentile16
Maximum33
Range32
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.885249206
Coefficient of variation (CV)0.8290821662
Kurtosis2.32865486
Mean5.892358327
Median Absolute Deviation (MAD)2
Skewness1.5531408
Sum139643
Variance23.86565981
MonotonicityNot monotonic
2022-04-16T21:43:06.599603image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
23368
14.2%
33073
13.0%
12917
12.3%
42804
11.8%
52621
11.1%
61305
 
5.5%
71218
 
5.1%
81083
 
4.6%
91051
 
4.4%
10687
 
2.9%
Other values (23)3572
15.1%
ValueCountFrequency (%)
12917
12.3%
23368
14.2%
33073
13.0%
42804
11.8%
52621
11.1%
61305
 
5.5%
71218
 
5.1%
81083
 
4.6%
91051
 
4.4%
10687
 
2.9%
ValueCountFrequency (%)
331
 
< 0.1%
321
 
< 0.1%
311
 
< 0.1%
301
 
< 0.1%
291
 
< 0.1%
281
 
< 0.1%
2710
 
< 0.1%
2624
 
0.1%
2546
0.2%
2463
0.3%

is_apartment
Boolean

MISSING

Distinct2
Distinct (%)0.1%
Missing20924
Missing (%)88.3%
Memory size740.9 KiB
False
2725 
True
 
50
(Missing)
20924 
ValueCountFrequency (%)
False2725
 
11.5%
True50
 
0.2%
(Missing)20924
88.3%
2022-04-16T21:43:06.693383image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

studio
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size23.3 KiB
False
23550 
True
 
149
ValueCountFrequency (%)
False23550
99.4%
True149
 
0.6%
2022-04-16T21:43:06.740200image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

open_plan
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size23.3 KiB
False
23632 
True
 
67
ValueCountFrequency (%)
False23632
99.7%
True67
 
0.3%
2022-04-16T21:43:06.787112image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

kitchen_area
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct971
Distinct (%)4.5%
Missing2278
Missing (%)9.6%
Infinite0
Infinite (%)0.0%
Mean10.5698072
Minimum1.3
Maximum112
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size185.3 KiB
2022-04-16T21:43:06.899900image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1.3
5-th percentile5.5
Q17
median9.1
Q312
95-th percentile20
Maximum112
Range110.7
Interquartile range (IQR)5

Descriptive statistics

Standard deviation5.905437934
Coefficient of variation (CV)0.5587081981
Kurtosis33.7611296
Mean10.5698072
Median Absolute Deviation (MAD)2.1
Skewness4.209631534
Sum226415.84
Variance34.87419719
MonotonicityNot monotonic
2022-04-16T21:43:07.040493image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
61300
 
5.5%
101262
 
5.3%
81110
 
4.7%
91101
 
4.6%
71062
 
4.5%
11797
 
3.4%
12662
 
2.8%
8.5415
 
1.8%
5.5400
 
1.7%
14381
 
1.6%
Other values (961)12931
54.6%
(Missing)2278
 
9.6%
ValueCountFrequency (%)
1.31
 
< 0.1%
27
< 0.1%
2.31
 
< 0.1%
2.41
 
< 0.1%
2.891
 
< 0.1%
37
< 0.1%
3.21
 
< 0.1%
3.31
 
< 0.1%
3.41
 
< 0.1%
3.54
< 0.1%
ValueCountFrequency (%)
1121
< 0.1%
1071
< 0.1%
100.71
< 0.1%
1001
< 0.1%
93.21
< 0.1%
931
< 0.1%
87.21
< 0.1%
772
< 0.1%
751
< 0.1%
721
< 0.1%

balcony
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing11519
Missing (%)48.6%
Infinite0
Infinite (%)0.0%
Mean1.150082102
Minimum0
Maximum5
Zeros3758
Zeros (%)15.9%
Negative0
Negative (%)0.0%
Memory size185.3 KiB
2022-04-16T21:43:07.165519image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile2
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.071300393
Coefficient of variation (CV)0.9314990568
Kurtosis2.505785898
Mean1.150082102
Median Absolute Deviation (MAD)1
Skewness1.243099425
Sum14008
Variance1.147684532
MonotonicityNot monotonic
2022-04-16T21:43:07.274857image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
14195
 
17.7%
03758
 
15.9%
23659
 
15.4%
5304
 
1.3%
4183
 
0.8%
381
 
0.3%
(Missing)11519
48.6%
ValueCountFrequency (%)
03758
15.9%
14195
17.7%
23659
15.4%
381
 
0.3%
4183
 
0.8%
5304
 
1.3%
ValueCountFrequency (%)
5304
 
1.3%
4183
 
0.8%
381
 
0.3%
23659
15.4%
14195
17.7%
03758
15.9%

locality_name
Categorical

HIGH CARDINALITY

Distinct364
Distinct (%)1.5%
Missing49
Missing (%)0.2%
Memory size2.5 MiB
Санкт-Петербург
15721 
посёлок Мурино
 
522
посёлок Шушары
 
440
Всеволожск
 
398
Пушкин
 
369
Other values (359)
6200 

Length

Max length55
Median length15
Mean length14.22169133
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique104 ?
Unique (%)0.4%

Sample

1st rowСанкт-Петербург
2nd rowпосёлок Шушары
3rd rowСанкт-Петербург
4th rowСанкт-Петербург
5th rowСанкт-Петербург

Common Values

ValueCountFrequency (%)
Санкт-Петербург15721
66.3%
посёлок Мурино522
 
2.2%
посёлок Шушары440
 
1.9%
Всеволожск398
 
1.7%
Пушкин369
 
1.6%
Колпино338
 
1.4%
посёлок Парголово327
 
1.4%
Гатчина307
 
1.3%
деревня Кудрово299
 
1.3%
Выборг237
 
1.0%
Other values (354)4692
 
19.8%

Length

2022-04-16T21:43:07.441726image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
санкт-петербург15721
54.5%
посёлок2108
 
7.3%
деревня945
 
3.3%
мурино590
 
2.0%
поселок552
 
1.9%
кудрово472
 
1.6%
шушары440
 
1.5%
всеволожск398
 
1.4%
пушкин369
 
1.3%
типа363
 
1.3%
Other values (330)6913
23.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

airports_nearest
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct8275
Distinct (%)45.6%
Missing5542
Missing (%)23.4%
Infinite0
Infinite (%)0.0%
Mean28793.67219
Minimum0
Maximum84869
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size185.3 KiB
2022-04-16T21:43:07.634301image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile11557.4
Q118585
median26726
Q337273
95-th percentile51340
Maximum84869
Range84869
Interquartile range (IQR)18688

Descriptive statistics

Standard deviation12630.88062
Coefficient of variation (CV)0.4386686261
Kurtosis-0.2883133015
Mean28793.67219
Median Absolute Deviation (MAD)9265
Skewness0.5409568907
Sum522806706
Variance159539145.3
MonotonicityNot monotonic
2022-04-16T21:43:07.781196image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3743461
 
0.3%
2192832
 
0.1%
3994630
 
0.1%
4487030
 
0.1%
3740727
 
0.1%
1873227
 
0.1%
3914026
 
0.1%
3174425
 
0.1%
3741224
 
0.1%
1949923
 
0.1%
Other values (8265)17852
75.3%
(Missing)5542
 
23.4%
ValueCountFrequency (%)
01
 
< 0.1%
64502
 
< 0.1%
69141
 
< 0.1%
69491
 
< 0.1%
69896
< 0.1%
69921
 
< 0.1%
69952
 
< 0.1%
70021
 
< 0.1%
70164
< 0.1%
70193
< 0.1%
ValueCountFrequency (%)
848691
< 0.1%
848531
< 0.1%
846651
< 0.1%
840061
< 0.1%
837581
< 0.1%
816071
< 0.1%
813551
< 0.1%
785271
< 0.1%
756461
< 0.1%
738271
< 0.1%

cityCenters_nearest
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct7642
Distinct (%)42.0%
Missing5519
Missing (%)23.3%
Infinite0
Infinite (%)0.0%
Mean14191.27783
Minimum181
Maximum65968
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size185.3 KiB
2022-04-16T21:43:08.048785image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum181
5-th percentile3541
Q19238
median13098.5
Q316293
95-th percentile31671.6
Maximum65968
Range65787
Interquartile range (IQR)7055

Descriptive statistics

Standard deviation8608.38621
Coefficient of variation (CV)0.6065969754
Kurtosis4.360911917
Mean14191.27783
Median Absolute Deviation (MAD)3483.5
Skewness1.674916144
Sum257997431
Variance74104313.14
MonotonicityNot monotonic
2022-04-16T21:43:08.196090image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
846061
 
0.3%
2080232
 
0.1%
1072030
 
0.1%
843427
 
0.1%
2044427
 
0.1%
837026
 
0.1%
1036426
 
0.1%
483625
 
0.1%
1736924
 
0.1%
1384523
 
0.1%
Other values (7632)17879
75.4%
(Missing)5519
 
23.3%
ValueCountFrequency (%)
1811
 
< 0.1%
2081
 
< 0.1%
2151
 
< 0.1%
2871
 
< 0.1%
2911
 
< 0.1%
3188
< 0.1%
3291
 
< 0.1%
3761
 
< 0.1%
3871
 
< 0.1%
3921
 
< 0.1%
ValueCountFrequency (%)
659681
< 0.1%
659521
< 0.1%
657641
< 0.1%
651051
< 0.1%
648571
< 0.1%
627061
< 0.1%
624541
< 0.1%
614951
< 0.1%
602231
< 0.1%
596261
< 0.1%

parks_around3000
Categorical

HIGH CORRELATION
MISSING

Distinct4
Distinct (%)< 0.1%
Missing5518
Missing (%)23.3%
Memory size1.3 MiB
0.0
10106 
1.0
5681 
2.0
1747 
3.0
 
647

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row1.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
0.010106
42.6%
1.05681
24.0%
2.01747
 
7.4%
3.0647
 
2.7%
(Missing)5518
23.3%

Length

2022-04-16T21:43:08.360131image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-16T21:43:08.438242image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.010106
55.6%
1.05681
31.2%
2.01747
 
9.6%
3.0647
 
3.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

parks_nearest
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct995
Distinct (%)12.3%
Missing15620
Missing (%)65.9%
Infinite0
Infinite (%)0.0%
Mean490.804555
Minimum1
Maximum3190
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size185.3 KiB
2022-04-16T21:43:08.567206image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile95.9
Q1288
median455
Q3612
95-th percentile968
Maximum3190
Range3189
Interquartile range (IQR)324

Descriptive statistics

Standard deviation342.3179949
Coefficient of variation (CV)0.6974629542
Kurtosis12.21768678
Mean490.804555
Median Absolute Deviation (MAD)163
Skewness2.717637751
Sum3965210
Variance117181.6096
MonotonicityNot monotonic
2022-04-16T21:43:08.754975image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
44167
 
0.3%
39241
 
0.2%
17341
 
0.2%
45640
 
0.2%
47132
 
0.1%
210230
 
0.1%
54129
 
0.1%
45829
 
0.1%
44728
 
0.1%
28828
 
0.1%
Other values (985)7714
32.5%
(Missing)15620
65.9%
ValueCountFrequency (%)
11
 
< 0.1%
31
 
< 0.1%
41
 
< 0.1%
71
 
< 0.1%
92
 
< 0.1%
107
< 0.1%
115
< 0.1%
121
 
< 0.1%
136
< 0.1%
141
 
< 0.1%
ValueCountFrequency (%)
31902
< 0.1%
30641
< 0.1%
30131
< 0.1%
29841
< 0.1%
29051
< 0.1%
28881
< 0.1%
28801
< 0.1%
28471
< 0.1%
27681
< 0.1%
27471
< 0.1%

ponds_around3000
Categorical

MISSING

Distinct4
Distinct (%)< 0.1%
Missing5518
Missing (%)23.3%
Memory size1.3 MiB
0.0
9071 
1.0
5717 
2.0
1892 
3.0
1501 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row0.0
3rd row2.0
4th row3.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.09071
38.3%
1.05717
24.1%
2.01892
 
8.0%
3.01501
 
6.3%
(Missing)5518
23.3%

Length

2022-04-16T21:43:08.975321image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-16T21:43:09.100295image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.09071
49.9%
1.05717
31.4%
2.01892
 
10.4%
3.01501
 
8.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

ponds_nearest
Real number (ℝ≥0)

MISSING

Distinct1096
Distinct (%)12.0%
Missing14589
Missing (%)61.6%
Infinite0
Infinite (%)0.0%
Mean517.9809001
Minimum13
Maximum1344
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size185.3 KiB
2022-04-16T21:43:09.321910image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile93
Q1294
median502
Q3729
95-th percentile976.55
Maximum1344
Range1331
Interquartile range (IQR)435

Descriptive statistics

Standard deviation277.7206427
Coefficient of variation (CV)0.5361600063
Kurtosis-0.7272670503
Mean517.9809001
Median Absolute Deviation (MAD)215
Skewness0.2220908711
Sum4718806
Variance77128.75537
MonotonicityNot monotonic
2022-04-16T21:43:09.593637image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42770
 
0.3%
45441
 
0.2%
15340
 
0.2%
43339
 
0.2%
56437
 
0.2%
47437
 
0.2%
30336
 
0.2%
44033
 
0.1%
35931
 
0.1%
73330
 
0.1%
Other values (1086)8716
36.8%
(Missing)14589
61.6%
ValueCountFrequency (%)
132
 
< 0.1%
168
< 0.1%
194
< 0.1%
205
< 0.1%
227
< 0.1%
231
 
< 0.1%
247
< 0.1%
251
 
< 0.1%
263
 
< 0.1%
273
 
< 0.1%
ValueCountFrequency (%)
13441
 
< 0.1%
13412
< 0.1%
13371
 
< 0.1%
13131
 
< 0.1%
12991
 
< 0.1%
12931
 
< 0.1%
12782
< 0.1%
12751
 
< 0.1%
12713
< 0.1%
12701
 
< 0.1%

days_exposition
Real number (ℝ≥0)

MISSING

Distinct1141
Distinct (%)5.6%
Missing3181
Missing (%)13.4%
Infinite0
Infinite (%)0.0%
Mean180.8886344
Minimum1
Maximum1580
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size185.3 KiB
2022-04-16T21:43:09.843578image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q145
median95
Q3232
95-th percentile647
Maximum1580
Range1579
Interquartile range (IQR)187

Descriptive statistics

Standard deviation219.7279882
Coefficient of variation (CV)1.214714174
Kurtosis6.27662008
Mean180.8886344
Median Absolute Deviation (MAD)68
Skewness2.310052616
Sum3711473
Variance48280.38878
MonotonicityNot monotonic
2022-04-16T21:43:10.080904image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45880
 
3.7%
60538
 
2.3%
7234
 
1.0%
30208
 
0.9%
90204
 
0.9%
4176
 
0.7%
3158
 
0.7%
5152
 
0.6%
14148
 
0.6%
9143
 
0.6%
Other values (1131)17677
74.6%
(Missing)3181
 
13.4%
ValueCountFrequency (%)
11
 
< 0.1%
23
 
< 0.1%
3158
0.7%
4176
0.7%
5152
0.6%
6124
0.5%
7234
1.0%
8139
0.6%
9143
0.6%
10127
0.5%
ValueCountFrequency (%)
15801
< 0.1%
15721
< 0.1%
15531
< 0.1%
15131
< 0.1%
15122
< 0.1%
14971
< 0.1%
14891
< 0.1%
14851
< 0.1%
14841
< 0.1%
14771
< 0.1%

Interactions

2022-04-16T21:42:57.159287image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2022-04-16T21:42:41.923135image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T21:42:44.271399image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T21:42:47.686757image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T21:42:50.288990image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T21:42:53.523368image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T21:42:56.489156image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T21:43:00.124486image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T21:42:17.859380image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T21:42:20.325915image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T21:42:23.208633image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T21:42:25.692035image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T21:42:28.141670image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T21:42:30.788484image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T21:42:33.359460image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T21:42:36.391915image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T21:42:39.477041image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T21:42:42.073185image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T21:42:44.423173image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T21:42:47.822263image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T21:42:50.435761image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T21:42:53.783246image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T21:42:56.656250image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T21:43:00.287449image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T21:42:18.009789image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T21:42:20.625671image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T21:42:23.356614image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T21:42:25.841551image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T21:42:28.304942image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T21:42:30.924592image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T21:42:33.504696image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T21:42:36.640247image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T21:42:39.604551image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T21:42:42.224008image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T21:42:44.701834image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T21:42:47.972363image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T21:42:50.604412image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T21:42:53.955579image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T21:42:56.804599image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T21:43:00.454609image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T21:42:18.172893image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T21:42:20.842111image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T21:42:23.525068image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T21:42:25.990550image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T21:42:28.554932image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T21:42:31.124495image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T21:42:33.678904image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T21:42:36.901141image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T21:42:39.770071image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T21:42:42.389524image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T21:42:44.937258image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T21:42:48.135856image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T21:42:50.772432image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T21:42:54.141012image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-16T21:42:56.969917image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2022-04-16T21:43:10.375485image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-04-16T21:43:10.930378image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-04-16T21:43:11.292706image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-04-16T21:43:11.731222image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-04-16T21:43:11.918632image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-04-16T21:43:00.783628image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-04-16T21:43:01.718892image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-04-16T21:43:02.263626image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-04-16T21:43:02.698103image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

Unnamed: 0total_imageslast_pricetotal_areafirst_day_expositionroomsceiling_heightfloors_totalliving_areaflooris_apartmentstudioopen_plankitchen_areabalconylocality_nameairports_nearestcityCenters_nearestparks_around3000parks_nearestponds_around3000ponds_nearestdays_exposition
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33064900000.0159.002015-07-24T00:00:003NaN14.0NaN9NaNFalseFalseNaN0.0Санкт-Петербург28098.06800.02.084.03.0234.0424.0
44210000000.0100.002018-06-19T00:00:0023.0314.032.0013NaNFalseFalse41.00NaNСанкт-Петербург31856.08098.02.0112.01.048.0121.0
55102890000.030.402018-09-10T00:00:001NaN12.014.405NaNFalseFalse9.10NaNгородской посёлок Янино-1NaNNaNNaNNaNNaNNaN55.0
6663700000.037.302017-11-02T00:00:001NaN26.010.606NaNFalseFalse14.401.0посёлок Парголово52996.019143.00.0NaN0.0NaN155.0
7757915000.071.602019-04-18T00:00:002NaN24.0NaN22NaNFalseFalse18.902.0Санкт-Петербург23982.011634.00.0NaN0.0NaNNaN
88202900000.033.162018-05-23T00:00:001NaN27.015.4326NaNFalseFalse8.81NaNпосёлок МуриноNaNNaNNaNNaNNaNNaN189.0
99185400000.061.002017-02-26T00:00:0032.509.043.607NaNFalseFalse6.502.0Санкт-Петербург50898.015008.00.0NaN0.0NaN289.0

Last rows

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236902369035500000.052.002018-07-19T00:00:002NaN5.031.02NaNFalseFalse6.00NaNСанкт-Петербург20151.06263.01.0300.00.0NaN15.0
2369123691119470000.072.902016-10-13T00:00:0022.7525.040.37NaNFalseFalse10.601.0Санкт-Петербург19424.04489.00.0NaN1.0806.0519.0
236922369221350000.030.002017-07-07T00:00:001NaN5.017.54NaNFalseFalse6.00NaNТихвинNaNNaNNaNNaNNaNNaN413.0
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2369523695143100000.059.002018-01-15T00:00:003NaN5.038.04NaNFalseFalse8.50NaNТосноNaNNaNNaNNaNNaNNaN45.0
2369623696182500000.056.702018-02-11T00:00:002NaN3.029.71NaNFalseFalseNaNNaNсело РождественоNaNNaNNaNNaNNaNNaNNaN
23697236971311475000.076.752017-03-28T00:00:0023.0017.0NaN12NaNFalseFalse23.302.0Санкт-Петербург39140.010364.02.0173.03.0196.0602.0
236982369841350000.032.302017-07-21T00:00:0012.505.012.31NaNFalseFalse9.00NaNпоселок Новый УчхозNaNNaNNaNNaNNaNNaNNaN